A method for detecting or ruling out non-small cell lung cancer (NS-CLC) in a patient comprises: (a) administering to a patient a detectable amount of a compound of formula (I): Formula (I) wherein the compound is targeted to any NSCLC tumor in the patient; and (b) acquiring an image to detect the presence or absence of any NSCLC tumor in the patient, wherein at least one of the atoms in formula (I) is replaced with .sup.11C, .sup.13N, .sup.15O, .sup.18F, .sup.34mCI, .sup.38K, .sup.45Ti, .sup.51Mn, .sup.52Mn, .sup.52Fe, .sup.55Co, .sup.60CU, .sup.61Cu, .sup.62Cu, .sup.64Cu, .sup.66Ga, .sup.68Ga, .sup.71As, .sup.72As, .sup.74As, .sup.75Br, .sup.75Br, .sup.76Br, .sup.82Rb, .sup.86Y, .sup.89Zr, .sup.90Nb, .sup.94mTc, .sup.110mIn, .sup.118Sb, .sup.120I, .sup.121I, .sup.122I, and .sup.124I.

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Provided herein are diffractometer-based global in situ diagnostic systems and uses thereof. The systems may comprise one or more tissue diffractometers that are configured for acquiring in situ diffraction data for a subject, e.g., a patient, and that are operatively coupled to a computer database over a network. The one or more tissue diffractometers may be configured for transfer of data such as image data, diffraction pattern data, subject data, or any combination thereof to the computer database over the network. The systems may further comprise one or more computer processors operatively coupled to the tissue diffractometers, which computer processors may be configured to receive the data from the tissue diffractometers, transmit the data to the computer database, and process the data using a data analytics algorithm which may provide a computer-aided diagnostic indicator for the individual subject.

A method and apparatus for tracking disease progression as revealed by multiple lesions perform a global optimization to identify corresponding lesions by overlap, for example, after outlines of the lesions have been morphologically dilated. A clustering algorithm addresses the problem of lesions separating into parts or joining together to provide a clear picture of disease progression.

Assessment of transthyretin amyloid cardiomyopathy (ATTR-CM) can be performed by calculating a cardiac pyrophosphate activity (CPA) measurement from single-photon emission computed tomography (SPECT) images. SPECT images obtained using .sup.99mTechnetium-pyrophosphate as a radiotracer can be analyzed to calculate CPA. Scan-specific thresholds for abnormal myocardial activity are identified based on left ventricular blood pool (LVBP) radiotracer counts, then radiotracer activity in regions of the myocardium with abnormal myocardial activity is determined. Finally, a CPA measurement can be calculated as a function of the mean radiotracer counts in such regions over the maximal LVBP radiotracer activity multiplied by the volume of involvement (e.g., the volume of abnormal activity). This CPA measurement can then be used as an assessment of ATTR-CM.

A method for processing breast tissue image data includes obtaining image data of a patient's breast tissue, processing the image data to generate a set of image slices, the image slices collectively depicting the patient's breast tissue; feeding image slices of the set through each of a plurality of object-recognizing modules, each of the object-recognizing modules being configured to recognize a respective type of object that may be present in the image slices; combining objects recognized by the respective object-recognizing modules to generate a synthesized image of the patient's breast tissue; and displaying the synthesized image.

A method for vascular assessment is disclosed. The method, in some embodiments, comprises receiving a plurality of 2-D angiographic images of a portion of a vasculature of a subject, and processing the images to produce a stenotic model over the vasculature, the stenotic model having measurements of the vasculature at one or more locations along vessels of the vasculature. The method, in some embodiments, further comprises obtaining a flow characteristic of the stenotic model, and calculating an index indicative of vascular function, based, at least in part, on the flow characteristic in the stenotic model.

Methods for quantitatively separating x-ray images of a subject having three or more component materials into component images using spectral imaging or multiple-energy imaging with 2D radiographic hardware implemented with scatter removal methods. The multiple-energy system may be extended by implementing DRC multiple energy decomposition and K-edge subtraction imaging methods.

A three-dimensional model for a patient fixation device that serves to immobilize at least a portion of a particular patient when capturing CT image information of that patient is accessed and then registered with the pixels that correspond to the patient fixation device in the CT image. The model can specify rules of movement for each of a plurality of structural elements that comprise the patient fixation device and that are capable of movement relative to one another. By one approach the aforementioned registration occurs on a part-by-part basis for each of the structural elements. Following registration, the CT image can be automatically segmented.

At least one processor is provided, in which the processor functions as a trained neural network that derives an estimation result relating to a composition of a soft tissue of a subject from a simple radiation image acquired by simply imaging the subject, or a DXA scanning image acquired by imaging the subject by a DXA method. The trained neural network learns using, as teacher data, two radiation images acquired by imaging the subject with radiation having different energy distributions, the radiation image of the subject and a soft part image representing the soft tissue of the subject, or a composite two-dimensional image representing the subject derived by combining a three-dimensional CT image of the subject, and information relating to the composition of the soft tissue of the subject.

A method of the present disclosure includes performing, by a processing device, a first image registration between a reference image of a patient and a motion image of the patient to perform alignment between the reference image and the motion image, wherein the reference image and the motion image include a target position of the patient. The method further includes performing, by the processing device, a second image registration between the reference image and a motion x-ray image of the patient, via a first digitally reconstructed radiograph (DRR) for the reference image of the patient. The method further includes tracking at least a translational change in the target position based on the first registration and the second registration.